📚 Livros
Última atualização: 22/08/2025
Sumário
📘Machine Learning
📘 Deep Learning
📘 LLMs, RAG e IA Generativa
📘 Estatística
📘 Programação em R
📘 Machine Learning
| N° | Título | Link |
|---|---|---|
| 1 | Aprendizado de Máquina: Uma Abordagem Estatística – Izbicki & Santos | [Link] |
| 2 | Inteligência Artificial e Aprendizagem de Máquina – Oscar Gabriel Filho | [Link] |
| 3 | Introdução ao Aprendizado de Máquina para Análise de Sobrevivência – Ara et al. | [Link] |
| 4 | Uma Introdução à Machine Learning – David Menotti (UFPR) | [Link] |
| 5 | A Course in Machine Learning – Hal Daumé III | [Link] |
| 6 | Mathematics for Machine Learning – Beginners – Mueller & Massaron | [Link] |
| 7 | ML Cheatsheet for Beginners – Jupyter Compilation | [Link] |
| 8 | Decision Tree Regressor Basics – Nikita Prasad | [Link] |
| 9 | Tutorials on Machine Learning & Deep Learning – Compilação técnica | [Link] |
| 10 | Algorithmic Mathematics in Machine Learning – Bohn, Garcke, Griebel | [Link] |
| 11 | Engineering MLOps – Emmanuel Raj | [Link] |
| 12 | Interpretable Machine Learning with Python – Serg Masís | [Link] |
| 13 | Machine Learning Applications: From Computer Vision to Robotics – Chatterjee & Zalte (Eds.) | [Link] |
| 14 | Machine Learning Using R – Brett Lantz | [Link] |
| 15 | A Minimal rTorch Book | https://f0nzie.github.io/rtorch-minimal-book/ |
| 16 | Applied Machine Learning for Tabular Data | https://aml4td.org/ |
| 17 | Applied Machine Learning Using mlr3 in R | https://mlr3book.mlr-org.com/ |
| 18 | Behavior Analysis with Machine Learning Using R | https://enriquegit.github.io/behavior-free/index.html# |
| 19 | Data Science: Theories, Models, Algorithms, and Analytics | https://srdas.github.io/MLBook/ |
| 20 | Deep Learning and Scientific Computing with R torch | https://skeydan.github.io/Deep-Learning-and-Scientific-Computing-with-R-torch/ |
| 21 | Efficient Machine Learning with R: Low-Compute Predictive Modeling with tidymodels | https://emlwr.org/ |
| 22 | Explanatory Model Analysis | https://pbiecek.github.io/ema/ |
| 23 | Feature Engineering A-Z | https://feaz-book.com/ |
| 24 | Feature Engineering and Selection: A Practical Approach for Predictive Models | http://www.feat.engineering/index.html |
| 25 | Hands-On Machine Learning with R | https://bradleyboehmke.github.io/HOML/ |
| 26 | Interpretable Machine Learning | https://leanpub.com/interpretable-machine-learning |
| 27 | Lightweight Machine Learning Classics with R | https://lmlcr.gagolewski.com/ |
| 28 | Machine Learning-based Causal Inference Tutorial | https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/ |
| 29 | Machine Learning for Factor Investing | http://www.mlfactor.com/ |
| 30 | Mathematics and Programming for Machine Learning with R: From the Ground Up | https://www.amazon.com/Mathematics-Programming-Machine-Learning-Ground-ebook-dp-B08JHDCX9Y/dp/B08JHDCX9Y |
| 31 | Model-Based Clustering, Classification and Density Estimation Using mclust in R | https://mclust-org.github.io/mclust-book/ |
| 32 | Neural Cryptography Using Keras in R | https://www.statswithr.com/neural-cryptography-using-keras-in-r |
| 33 | Neural Networks with Keras in R: A QuickStart Guide | https://www.statswithr.com/neural-networks-with-keras-in-r-a-quickstart-guide |
| 34 | sits: Data Analysis and Machine Learning on Earth Observation Data Cubes with Satellite Image Time Series | https://e-sensing.github.io/sitsbook/ |
| 35 | Supervised Machine Learning for Text Analysis in R | https://smltar.com/ |
| 36 | Surrogates – Gaussian Process Modeling, Design and Optimization for the Applied Sciences | https://bookdown.org/rbg/surrogates/ |
| 37 | The caret Package | https://topepo.github.io/caret/index.html |
| 38 | The Hitchhiker’s Guide to Responsible Machine Learning | https://betaandbit.github.io/RML/ |
| 39 | Tidy Modeling with R | https://www.tmwr.org/ |
📘 Deep Learning
| Nº | Título | Link |
|---|---|---|
| 1 | Deep Learning for the Life Sciences – Ramsundar, Eastman, Walters, Pande | [Link] |
| 2 | Time Series Analysis & Machine Learning for Predictive Modeling – Rathod et al. | [Link] |
| 3 | Tutorials on Machine Learning & Deep Learning – Compilação técnica | [Link] |
📘 LLMs, RAG e IA Generativa
| Nº | Título | Link |
|---|---|---|
| 1 | Unlocking Data with Generative AI and RAG – Keith Bourne | [Link] |
| 2 | Building LLMs for Production – Sinan Ozdemir | [Link] |
| 3 | Mastering LLM Applications with LangChain and Hugging Face – Eric Sarrion, Julien Simon | [Link] |
| 4 | Generative AI for Cloud Solutions Architect – Maddie Schieferstein | [Link] |
📘 Estatística
🔹 Fundamentos e Introduções
| Nº | Título | Link |
|---|---|---|
| 1 | An Introduction to Statistical and Data Sciences via R | https://moderndive.com/ |
| 2 | An Introduction to Statistical Learning | https://www.statlearning.com/ |
| 3 | Introduction to Modern Statistics | https://openintro-ims.netlify.app/ |
| 4 | OpenIntro Statistics | https://leanpub.com/openintro-statistics |
| 5 | Statistics (The Easier Way) With R, 3rd Ed. | https://amzn.to/3b9ha8s |
| 6 | Statistics and Data with R | https://www.wiley.com/en-us/Statistics+and+Data+with+R%3A+An+Applied+Approach+Through+Examples-p-9780470758052 |
| 7 | Answering questions with data | https://crumplab.github.io/statistics/ |
🔹 Bayes e Probabilidade
| Nº | Título | Link |
|---|---|---|
| 1 | Bayes rules! | https://www.bayesrulesbook.com/ |
| 2 | An Introduction to Bayesian Data Analysis for Cognitive Science | https://vasishth.github.io/bayescogsci/book/ |
| 3 | An Introduction to Bayesian Reasoning and Methods | https://bookdown.org/kevin_davisross/bayesian-reasoning-and-methods/ |
| 4 | Doing Bayesian Data Analysis in brms and the tidyverse | https://bookdown.org/content/3686/ |
| 5 | Bayesian analysis of capture-recapture data | https://oliviergimenez.github.io/banana-book/index.html |
| 6 | Introduction to Empirical Bayes | https://drob.gumroad.com/l/empirical-bayes |
| 7 | Probability and Bayesian Modeling | https://bayesball.github.io/BOOK/probability-a-measurement-of-uncertainty.html |
| 8 | Statistical Rethinking | https://xcelab.net/rm/statistical-rethinking/ |
| 9 | Statistical Rethinking with brms, ggplot2, and the tidyverse | https://bookdown.org/content/4857/ |
| 10 | Using R for Bayesian Spatial and Spatio-Temporal Health Modeling | https://www.routledge.com/… |
🔹 Regressão e GLM
| Nº | Título | Link |
|---|---|---|
| 1 | Advanced Regression Methods – Companion to BER642 | https://bookdown.org/chua/ber642_advanced_regression/ |
| 2 | Analysing Data using Linear Models | https://bookdown.org/pingapang9/linear_models_bookdown/ |
| 3 | Beyond Multiple Linear Regression | https://bookdown.org/roback/bookdown-BeyondMLR/ |
| 4 | Flexible Regression Models | https://discdown.org/flexregression/ |
| 5 | Handbook of Regression Modeling in People Analytics | http://peopleanalytics-regression-book.org/index.html |
| 6 | Introduction to Regression Analysis in R | https://www.kellerbiostat.com/introregression/ |
| 7 | Regression and Other Stories | https://avehtari.github.io/ROS-Examples/ |
| 8 | The Hitchhiker’s Guide to Linear Models | https://leanpub.com/linear-models-guide |
🔹 Inferência
| Nº | Título | Link |
|---|---|---|
| 1 | Common statistical tests are linear models | https://steverxd.github.io/Stat_tests/ |
| 2 | Statistical inference for data science | https://leanpub.com/LittleInferenceBook |
| 3 | Foundations of Statistics with R | https://mathstat.slu.edu/~speegle/_book/preface.html |
| 4 | Statistical Thinking in the 21st Century | https://statsthinking21.github.io/statsthinking21-core-site/ |
🔹 Longitudinal e Mistos
| Nº | Título | Link |
|---|---|---|
| 1 | Applied longitudinal data analysis in brms and the tidyverse | https://bookdown.org/content/4253/ |
| 2 | Mixed Models with R | https://m-clark.github.io/mixed-models-with-R/ |
🔹 Meta-análise e Poder
| Nº | Título | Link |
|---|---|---|
| 1 | Doing meta-analysis with R | https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/ |
| 2 | Power Analysis with Superpower | https://aaroncaldwell.us/SuperpowerBook/index.html |
| 3 | Introduction to Mediation, Moderation, and Conditional Process Analysis | https://bookdown.org/content/b472c7b3-ede5-40f0-9677-75c3704c7e5c/ |
🔹 Causalidade
| Nº | Título | Link |
|---|---|---|
| 1 | Causal Inference in R | https://www.r-causal.org |
| 2 | Causal Inference: The Mixtape | https://mixtape.scunning.com/ |
| 3 | The Effect: An Introduction to Research Design and Causality | https://theeffectbook.net/ |
🔹 Séries Temporais e Multivariada
| Nº | Título | Link |
|---|---|---|
| 1 | A Little Book of R for Time Series | https://a-little-book-of-r-for-time-series.readthedocs.io |
| 2 | A Little Book of R for Multivariate Analysis | https://little-book-of-r-for-multivariate-analysis.readthedocs.io |
🔹 Didáticos e Catálogos
| Nº | Título | Link |
|---|---|---|
| 1 | End-to-End Solved Problems With R | https://amzn.to/2EREAn2 |
| 2 | Model Estimation by Example | https://m-clark.github.io/models-by-example/ |
| 3 | Library of Statistical Techniques | https://lost-stats.github.io/ |
| 4 | ISLR tidymodels Labs | https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/index.html |
| 5 | Marginal Effects Zoo | https://marginaleffects.com |
| 6 | Teacups, Giraffes and Statistics | https://tinystats.github.io/teacups-giraffes-and-statistics/index.html |
| 7 | The Saga of PLS | https://sagaofpls.github.io |
| 8 | Translating Stata to R | https://stata2r.github.io/ |
🔹 Áreas Aplicadas
| Nº | Título | Link |
|---|---|---|
| 1 | Building energy statistical modelling | https://buildingenergygeeks.org/index.html |
| 2 | Modern Statistical Methods for Astronomy | https://www.cambridge.org/… |
| 3 | Spatio-Temporal Statistics with R | https://spacetimewithr.org/ |
| 4 | Surrogates – Gaussian process modeling | https://bookdown.org/rbg/surrogates/ |
| 5 | Partial Least Squares Structural Equation Modeling (PLS-SEM) | https://link.springer.com/book/10.1007/978-3-030-80519-7 |
| 6 | The Grammar of Experimental Designs | https://emitanaka.org/edibble-book/index.html |
| 7 | One Way ANOVA with R | https://bcdudek.net/anova/oneway_anova_basics.pdf |
| 8 | Data Analytics | https://discdown.org/dataanalytics/ |
| 9 | R for Data Analytics | https://rforanalytics.com/ |
| 10 | A Business Analyst’s Introduction to Business Analytics | https://www.causact.com/ |
📘 Programação em R
| Nº | Título | Link |
|---|---|---|
| 1 | R para Ciência de Dados (2ª edição) – Português | [Link] |
| 2 | Ciência de Dados em R – Cursos R | [Link] |
| 3 | Introdução à Linguagem R: seus fundamentos e sua prática – Pedro Duarte Faria | [Link] |
| 4 | Statistical Inference via Data Science – Ismay & Kim | [Link] |
| 5 | ggplot2: Elegant Graphics for Data Science – Hadley Wickham | [Link] |
| 6 | R for Data Science (1ª ed): Exercise Solutions – Jeffrey B. Arnold | [Link] |
| 7 | Data Manipulation in R – Steph Locke | [Link] |